NUC optimization for Hierarchical Modulation aiming at achieving comparable capacity with Layered Division Multiplexing
Bibliographic record
Abstract
This paper investigates the non-uniform constellation (NUC) optimization adapted for Hierarchical modulation (HM) without using Successive Interference Cancellation (SIC). This approach reduces system demod/decode delay in comparison to Layered Division Multiplexing (LDM). The objective is enabling the capacity achieved by HM comparable to LDM. To achieve this goal, the constellation constrained capacity of Enhanced Layer (EL) service in HM is maximized, while the capacity of Core Layer (CL) service are approximately the same in HM and LDM. Particle Swarm optimization (PSO) algorithm is used to solve this problem. To accelerate the optimization, initial constellation is selected from regular NUCs or the combination of CL and EL constellations of LDM in ATSC 3.0. The results imply that under certain capacity demands, especially when there is a large difference between the SNR thresholds for correct decoding of CL and EL or the power ratio of CL to EL is high (for example, 10 dB or higher), HM, with lower delay compared to LDM, can achieve capacity close to LDM with the help of NUC. Even if the power ratio of CL to EL is relatively low (for example, 3 dB), the capacity loss can be reduced with properly designed NUC and the SNR threshold loss of EL can be lower than 1 dB with respect to LDM. However, LDM is still superior to HM when the difference between the SNR thresholds of CL and EL is relatively low.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".